Analysis of AUV Signals

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Author
Rowe, Neil C.
Schwamm, Riqui
Allen, Bruce D.
Kalinowski, Pawel
Date
2019Metadata
Show full item recordAbstract
We were tasked to assess the suitability of deep-learning methods for complex high-frequency signals such
as were produced by recent automated underwater vehicles. Such vehicles transmit detailed data that is
considerably more complex than traditional sensors. We interpreted the task as including several subgoals.
First, we need to determine distinctive features of these signals. Second, we need to distinguish different
signal sources from each other. Third, we need to distinguish periods of time within those signals and
make guesses as to what is happening in each. We used an approach of extracting features from both the
time domain (wavelets were the most helpful) and the frequency domain (logarithmically spaced frequency
components were the most helpful). We trained several kinds of machine-learning models and
demonstrated excellent performance in distinguishing the test signals.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.NPS Report Number
NPS-CS-19-001
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